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Artificial Intelligence and Big Data for Oceanography

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 5368

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Special Issue Editors

Prof. Dr. Peng Ren

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Guest Editor

College of Oceanography and Space Informatics, China University of Petroleum, 66 Changjiang West Road, Qingdao 266580, China
Interests: remote sensing; oceanic engineering; machine learning; signal processing

Dr. Yongqing Li

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Guest Editor

School of Data Science, Qingdao University of Science and Technology, Qingdao 266061, China
Interests: remote sensing; oceanic disaster prediction; machine learning

Prof. Dr. Weimin Huang

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Guest Editor

Department of Electrical and Computer Engineering, Memorial University, St. John’s, NL A1B 3X5, Canada
Interests: mapping of oceanic surface parameters via high-frequency ground wave radar; X-band marine radar and global navigation satellite systems
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Special Issue Information

Dear Colleagues,

Oceanography refers to the scientific study of the oceans. It involves multiple disciplines such as astronomy, biology, chemistry, climatology, geography, geology, hydrology, meteorology and physics. In recent decades, with the development of remote sensing and other observation technology, oceanography has been extensively enriched by the amount and variety of observation data. This big data enables state-of-the-art artificial intelligence methods to further increase the depth and width of oceanography. Artificial intelligence methods can effectively mine useful ocean information from a large amount of oceanographic observation data. Recent studies have shown the advantages of the artificial intelligence methods in terms of processing oceanographic data. Therefore, oceanography incorporating artificial intelligence and big data is an important research topic.

This Special Issue aims at studies about artificial intelligence and big-data based oceanography. The studies may cover the acquisition of big observation data, design of artificial intelligence models, analysis of specific oceanographic issues, and other related topics. The collection of oceanographic data is mainly conducted using remote sensing technology. The journal encourages artificial intelligence methods for processing remote sensing data. Hence, the subject is closely related to the journal scope.

Articles may address, but are not limited, to the following topics related to artificial intelligence and big data:

Oceanographic data acquisition;
Meteorological forecast;
Oceanic disaster prediction;
Climate anomaly warning;
Multisource meteorological observation;
Oceanic information extraction;
Oil spill trajectory prediction;
Sea ice detection and prediction;
Algal bloom detection and prediction;
Mesoscale eddy detection;
Internal ocean wave detection;
Coastal remote sensing.

Prof. Dr. Peng Ren
Dr. Yongqing Li
Prof. Dr. Weimin Huang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

artificial intelligence
oceanography
big data
remote sensing
information extraction
data mining

Published Papers (6 papers)

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Research

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16 pages, 24589 KiB

Open AccessArticle

Prediction of Sea Surface Temperature Using U-Net Based Model

by Jing Ren, Changying Wang, Ling Sun, Baoxiang Huang, Deyu Zhang, Jiadong Mu and Jianqiang Wu

Remote Sens. 2024, 16(7), 1205; https://doi.org/10.3390/rs16071205 - 29 Mar 2024

Viewed by 519

Abstract

Sea surface temperature (SST) is a key parameter in ocean hydrology. Currently, existing SST prediction methods fail to fully utilize the potential spatial correlation between variables. To address this challenge, we propose a spatiotenporal UNet (ST-UNet) model based on the UNet model. In particular, in the encoding phase of ST-UNet, we use parallel convolution with different kernel sizes to efficiently extract spatial features, and use ConvLSTM to capture temporal features based on the utilization of spatial features. Atrous Spatial Pyramid Pooling (ASPP) module is placed at the bottleneck of the network to further incorporate the multi-scale features, allowing the spatial features to be fully utilized. The final prediction is then generated in the decoding stage using parallel convolution with different kernel sizes similar to the encoding stage. We conducted a series of experiments on the Bohai Sea and Yellow Sea SST data set, as well as the South China Sea SST data set, using SST data from the past 35 days to predict SST data for 1, 3, and 7 days in the future. The model was trained using data spanning from 2010 to 2021, with data from 2022 being utilized to assess the model’s predictive performance. The experimental results show that the model proposed in this research paper achieves excellent results at different prediction scales in both sea areas, and the model consistently outperforms other methods. Specifically, in the Bohai Sea and Yellow Sea sea areas, when the prediction scales are 1, 3, and 7 days, the MAE of ST-UNet outperforms the best results of the other three compared models by 17%, 12%, and 2%, and the MSE by 16%, 18%, and 9%, respectively. In the South China Sea, when the prediction ranges are 1, 3, and 7 days, the MAE of ST-UNet is 27%, 18%, and 3% higher than the best of the other three compared models, and the MSE is 46%, 39%, and 16% higher, respectively. Our results highlight the effectiveness of the ST-UNet model in capturing spatial correlations and accurately predicting SST. The proposed model is expected to improve marine hydrographic studies. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data for Oceanography)

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19 pages, 3675 KiB

Open AccessArticle

Stripe Extraction of Oceanic Internal Waves Using PCGAN with Small-Data Training

by Bohuai Duan, Saheya Barintag, Junmin Meng and Maoguo Gong

Remote Sens. 2024, 16(5), 787; https://doi.org/10.3390/rs16050787 - 24 Feb 2024

Viewed by 453

Abstract

Playing a crucial role in ocean activities, internal solitary waves (ISWs) are of significant importance. Currently, the use of deep learning for detecting ISWs in synthetic aperture radar (SAR) imagery is gaining growing attention. However, these approaches often demand a considerable number of labeled images, which can be challenging to acquire in practice. In this study, we propose an innovative method employing a pyramidal conditional generative adversarial network (PCGAN). At each scale, it employs the framework of a conditional generative adversarial network (CGAN), comprising a generator and a discriminator. The generator works to produce internal wave patterns as authentically as possible, while the discriminator is designed to differentiate between images generated by the generator and reference images. The architecture based on pyramids adeptly captures the encompassing as well as localized characteristics of internal waves. The incorporation of upsampling further bolsters the model’s ability to recognize fine-scale internal wave stripes. These attributes endow the PCGAN with the capacity to learn from a limited amount of internal wave observation data. Experimental results affirm that the PCGAN, trained with just four internal wave images, can accurately detect internal wave stripes in the test set. Through comparative experiments with other segmentation models, we demonstrate the effectiveness and robustness of PCGAN. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data for Oceanography)

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24 pages, 6030 KiB

Open AccessArticle

A Method for Estimating Ship Surface Wind Parameters by Combining Anemometer and X-Band Marine Radar Data

by Yuying Zhang, Zhizhong Lu, Congying Tian, Yanbo Wei and Fanming Liu

Remote Sens. 2023, 15(22), 5392; https://doi.org/10.3390/rs15225392 - 17 Nov 2023

Viewed by 699

Abstract

The steady airflow field on a ship is affected by structure and motion and challenged by phenomena such as the low measurement accuracy of the wind field caused by the occlusion of the anemometer. In this work, an improvement in the accuracy of wind measurements affected by structure is proposed, and a method for combining anemometer and X-band marine radar (RCRF) data is designed to further obtain wind parameters. The first step is to use the multivariate bias strategy to achieve the optimal layout of multiple anemometers based on computational fluid dynamics (CFD) numerical simulation data. Then, random forest (RF) is employed to train the wind parameter estimation model. Finally, the wind parameters are optimally estimated by combining the anemometer with the X-band radar. Under the ideal simulation, noise, and temporal uncertainty combined with anemometer noise conditions, the RCRF algorithm performance is evaluated. Compared with the bias correction combination four-anemometer weighted fusion algorithm (FAF-BC) and the BP neural network algorithm for radar wind measurement combination (RCBP), the mean errors in wind direction and speed are reduced by 1.99° and 6.99% at most. The maximum errors are reduced by 14.46° and 15.81% at most, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data for Oceanography)

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25 pages, 1371 KiB

Open AccessArticle

A Method of Extracting the SWH Based on a Constituted Wave Slope Feature Vector (WSFV) from X-Band Marine Radar Images

by Yanbo Wei, Yujie Wang, Chendi He, Huili Song, Zhizhong Lu and Hui Wang

Remote Sens. 2023, 15(22), 5355; https://doi.org/10.3390/rs15225355 - 14 Nov 2023

Viewed by 674

Abstract

The shadow statistical method (SSM) used for extracting the significant wave height (SWH) from X-band marine radar images was further investigated because of its advantage of not requiring an external reference for calibration. Currently, a fixed shadow segmentation threshold is utilized to extract the SWH from a radar image based on the SSM. However, the retrieval accuracy of the SWH is not ideal for low wind speeds since the echo intensity of sea waves rapidly decays over distance. In order to solve this problem, an adaptive shadow threshold, which varies with echo intensity over distance and can accurately divide the radar image into shadow and nonshadow areas, is adopted to calculate the wave slope (WS) based on the texture feature of the edge image. Instead of using the averaged WS, the wave slope feature vector (WSFV) is constructed for retrieving the SWH since the illumination ratio and the calculated WS in the azimuth are different for shore-based radar images. In this paper, the SWH is calculated based on the constructed WSFV and classical support vector regression (SVR) technology. The collected 222 sets of X-band marine radar images with an SWH range of 1.0∼3.5 m and an average wind speed range of 5∼10 m/s were utilized to verify the performance of the proposed approach. The buoy record, which was deployed during the experiment, was used as the ground truth. For the proposed approach, the mean bias (BIAS) and the mean absolute error (MAE) were 0.03 m and 0.14 m when the ratio of the training set to the test set was 1:1. Compared to the traditional SSM, the correlation coefficient (CC) of the proposed approach increased by 0.27, and the root mean square error (RMSE) decreased by 0.28 m. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data for Oceanography)

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21 pages, 5828 KiB

Open AccessArticle

Artificial Intelligence Forecasting of Marine Heatwaves in the South China Sea Using a Combined U-Net and ConvLSTM System

by Wenjin Sun, Shuyi Zhou, Jingsong Yang, Xiaoqian Gao, Jinlin Ji and Changming Dong

Remote Sens. 2023, 15(16), 4068; https://doi.org/10.3390/rs15164068 - 17 Aug 2023

Cited by 8 | Viewed by 1660

Abstract

Marine heatwaves (MHWs) are extreme events characterized by abnormally high sea surface temperatures, and they have significant impacts on marine ecosystems and human society. The rapid and accurate forecasting of MHWs is crucial for preventing and responding to the impacts they can lead to. However, the research on relevant forecasting methods is limited, and a dedicated forecasting system specifically tailored for the South China Sea (SCS) region has yet to be reported. This study proposes a novel forecasting system utilizing U-Net and ConvLSTM models to predict MHWs in the SCS. Specifically, the U-Net model is used to forecast the intensity of MHWs, while the ConvLSTM model is employed to predict the probability of their occurrence. The indication of an MHW relies on both the intensity forecasted by the U-Net model exceeding threshold T and the occurrence probability predicted by the ConvLSTM model surpassing threshold P. Incorporating sensitivity analysis, optimal thresholds for T are determined as 0.9 °C, 0.8 °C, 1.0 °C, and 1.0 °C for 1-, 3-, 5-, and 7-day forecast lead times, respectively. Similarly, optimal thresholds for P are identified as 0.29, 0.30, 0.20, and 0.28. Employing these thresholds yields the highest forecast accuracy rates of 0.92, 0.89, 0.88, and 0.87 for the corresponding forecast lead times. This innovative approach gives better predictions of MHWs in the SCS, providing invaluable reference information for marine management authorities to make well-informed decisions and issue timely MHW warnings. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data for Oceanography)

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14 pages, 4604 KiB

Open AccessTechnical Note

Anisotropic Green Tide Patch Information Extraction Based on Deformable Convolution

by Binge Cui, Mengting Liu, Ruipeng Chen, Haoqing Zhang and Xiaojun Zhang

Remote Sens. 2024, 16(7), 1162; https://doi.org/10.3390/rs16071162 - 27 Mar 2024

Viewed by 439

Abstract

Green tides are marine disasters caused by the explosive proliferation or high concentration of certain large algae in seawater, which causes discoloration of the water body. Accurate monitoring of its distribution area is highly important for early warning and the protection of marine ecology. However, existing deep learning methods have difficulty in effectively identifying green tides with anisotropic characteristics due to the complex and variable shapes of the patches and the wide range of scales. To address this issue, this paper presents an anisotropic green tide patch extraction network (AGE-Net) based on deformable convolution. The main structure of AGE-Net consists of stacked anisotropic feature extraction (AFEB) modules. Each AFEB module contains two branches for extracting green tide patches. The first branch consists of multiple connected dense blocks. The second branch introduces a deformable convolution module and a depth residual module based on a multiresolution feature extraction network for extracting anisotropic features of green tide patches. Finally, an irregular green tide patch feature enhancement module is used to fuse the high-level semantic features extracted from the two branches. To verify the effectiveness of the AGE-Net model, experiments were conducted on the MODIS Green Tide dataset. The results show that AGE-Net has better recognition performance, with F1-scores and IoUs reaching 0.8317 and 71.19% on multi-view test images, outperforming other comparison methods. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data for Oceanography)

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